Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 374 · 40 questions

AI tip (Show, label, learn): Machine learning learns patterns from many labelled examples — a label is the right answer tag on each example. Good for training: many varied examples, correct labels, every type the model will meet, and separate new data for testing. Bad for training: very few examples, wrong or random labels, only one kind of example, blurry examples, testing on the training data.

1. 1.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
2. 2.Which of these is bad for training a model? (a) labels added at random (b) examples of every type (c) many varied examples (d) checking labels twice
3. 3.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
5. 5.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
6. 6.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) slower to switch on (d) change colour
7. 7.Which of these is good for training a model? (a) photos in many lights (b) labels added at random (c) wrong labels (d) very few examples
8. 8.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
9. 9.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) blurry, unclear photos (d) many varied examples
11. 11.🔍 Which is the odd one out? (a) wrong labels (b) copies of one photo (c) only one kind of example (d) correct labels
12. 12.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
13. 13.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) clear, sharp examples (d) very few examples
14. 14.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
15. 15.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
16. 16.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) examples of every type (d) only one kind of example
17. 17.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it will be perfect (d) it may get it wrong
18. 18.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) testing on training data (d) examples of every type
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
20. 20.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
21. 21.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
22. 22.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
23. 23.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
24. 24.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
25. 25.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
26. 26.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
27. 27.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
28. 28.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
29. 29.🔍 Which is the odd one out? (a) very few examples (b) photos in many lights (c) wrong labels (d) labels added at random
30. 30.Which of these is bad for training a model? (a) clear, sharp examples (b) copies of one photo (c) new data for testing (d) many varied examples
31. 31.Which of these is bad for training a model? (a) new data for testing (b) testing on training data (c) many varied examples (d) photos in many lights
32. 32.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
33. 33.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
34. 34.🔍 Which is the odd one out? (a) wrong labels (b) photos in many lights (c) many varied examples (d) checking labels twice
35. 35.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
36. 36.True or false: “correct labels” is good for training a model. (a) True (b) False
37. 37.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
38. 38.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
39. 39.Which of these is good for training a model? (a) wrong labels (b) clear, sharp examples (c) testing on training data (d) only one kind of example
40. 40.True or false: “new data for testing” is good for training a model. (a) False (b) True

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